Abstract

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises.

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